Machine Learning Systems Engineer
Tasks
- Architect backend services for media intelligence platform
- Build scalable media ingestion and processing pipelines
- Build vector search indexing and retrieval workflows
- Deploy and operate systems on cloud platforms
- Design data models for media assets and metadata
- Implement asynchronous processing with retries idempotency and dead letter queues
- Implement event driven workflows with queues and pub sub
- Integrate and optimize AI ML services in production workflows
- Lead metadata extraction content analysis transcription and embedding workflows
- Maintain model serving and performance with batching caching and warmup
- Monitor production performance and reliability
- Optimize model inference for latency throughput reliability and cost
- Own backend components end-to-end
Perks/Benefits
Skills/Tech-stack
AWS | AWS SageMaker | Alerting | Autoscaling | Azure | Batching | Caching | Data Modeling | Database Design | Distributed Systems | Elasticsearch | Embeddings | Event Driven | Event-driven architecture | Faiss | GPU Utilization | Google Cloud | Google Vertex | Google Vertex AI | Hugging Face | Hugging Face Transformers | Logging | Message Queues | Metrics | Microservices | Model Distillation | NVIDIA Triton | NoSQL | Node.js | OCR | Observability | PGVector | Pinecone | Pub/Sub | PyTorch | Python | Qdrant | Quantization | REST APIs | SQL | Semantic Search | Speech-to-Text | System design | TensorFlow | Text Generation | Text generation inference | Tracing | Transcription | VLLM | Vector Search | Vertex AI | Weaviate
Education
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